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Machine learning to predict extubation success using the spontaneous breathing trial, objective cough measurement, and diaphragmatic contraction velocity: Secondary analysis of the COBRE-US trial

Fabio Varón-Vega

Varón-Vega, Fabio

,

Eduardo Tuta-Quintero

Tuta-Quintero, Eduardo

,

Adriana Maldonado-Franco

Maldonado-Franco, Adriana

,

Henry Robayo-Amórtegui

Robayo-Amórtegui, Henry

,

Luis F Giraldo-Cadavid

Giraldo-Cadavid, Luis F

and

Daniel Botero-Rosas

Botero-Rosas, Daniel

Jan 31, 2025

Machine learning to predict extubation success using the spontaneous breathing trial, objective cough measurement, and diaphragmatic contraction velocity: Secondary analysis of the COBRE-US trial's Cover Image

The Journal of Critical Care Medicine

Volume 11 (2025): Issue 1 (January 2025)

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Article Category: Research Article

Published Online: Jan 31, 2025

Page range: 70 - 77

Received: Oct 22, 2024

Accepted: Jan 26, 2025

DOI: https://doi.org/10.2478/jccm-2025-0009

Keywords
critical illness, airway extubation, ventilator weaning, machine learning

© 2025 Fabio Varón-Vega et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Machine Learning Methods for Predicting Success in Spontaneous Breathing Trial

Model	Study Variable	Accuracy	Sensitivity	Specificity	PPV	NVP
k-means	SBT^* training	64,0	72,6	31,5	79,9	23,5
	SBT ^* test	63,0	72,0	35,7	72,7	38,5

Hierarchical Clustering	SBT ^* training	52,7	53,3	50,7	80,2	22,4
	SBT ^* test	60,9	54,9	64,3	79,2	40,9

Decision Trees	SBT ^* training	77,3	99,9	1,1	NI	1,0
	SBT ^* test	69,6	99,9	1,0	NI	1,0

Support Vector Machines	SBT ^* training	77,3	99,9	1,1	NI	1,0
	SBT ^* test	69,6	99,9	1,1	NI	1,0

Neural Networks	SBT ^* training	77,3	99,9	1,0	NI	1,0
	SBT ^* test	69,6	99,9	1,0	NI	1,0

Qualification	Description
0	No cough
1	Audible movement of air through the endotracheal tube, but no audible cough
2	Strong cough with movement of secretions into the endotracheal tube
3	Strong cough with movement of secretions out (expulsion) of the endotracheal tube

Etiology of Respiratory Failure and Reason for Admission to Intensive Care

Variables	Values
Shock, n(%)	52 (14,9)
Hypercapnia (pH < 7,25, CO2 elevated), n(%)	23 (6,6)
Hypoxemia (PaO2 < 60, usual FiO2), n(%)	261 (75)
Neuromuscular, n(%)	2 (0,6)
Perioperative, n(%)	10 (2,9)

Reason for ICU Admission, n (%)
Medical	345 (94)
Surgical (post-surgical only)	22 (6)

General Characteristics of the Population_

Variables n (%)	Values
Male n (%)	219 (59,7)
Age, median (Range)	61 (18 – 88)
Weight in kg, median (IQR)	70 (60 – 80)
Height in cm, mean (SD)	163,6 (10)
Body Mass Index (BMI) in kg/m²,
median (IQR)	25,3 (21,7 – 29,1)
Active smoking, n (%)	33 (9)
Alcoholism n (%)	22 (6)

Comorbidities, n (%)
Diabetes Mellitus	113 (30,8)
Hypertension	173 (47,1)
Asthma	8 (2,2)
Pulmonary Fibrosis	6 (1,6)
Chronic Kidney Disease	69 (18,8)
Chronic Liver Disease	17 (4,6)

Machine Learning Methods for Predicting Extubation Success

Model	Study Variable	Accuracy	Sensitivity	Specificity	PPV	NVP
k-means	SBT^* training	63,4	74,4	35,1	74,7	34,7
	SBT ^* test	63,0	76,7	37,5	69,8	46,2

Hierarchical Clustering	SBT ^* training	66,4	91,6	8,0	69,7	31,4
	SBT ^* test	65,2	90,0	18,8	67,5	50

Decision Trees	SBT ^* training	89,8	98,3	70,4	94,6	68,7
	SBT ^* test	95,7	99,9	87,5	99,9	68,7

Support Vector Machines	SBT ^* training	85,9	99,0	56,0	95,9	55
	SBT ^* test	93,5	99,9	81,3	99,9	81,3

Neural Networks	SBT ^* training	85,9	99,0	56,0	95,9	55
	SBT ^* test	93,5	99,9	81,3	99,9	81,3

Language:: English

Publication timeframe:: 4 times per year
Journal Subjects:: Medicine, Clinical Medicine, Internal Medicine, Internal Medicine, other, Surgery, Surgery, other, Anaesthesiology, Emergency Medicine and Intensive-Care Medicine

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